UniMark: Artificial Intelligence Generated Content Identification Toolkit
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910095794765824 |
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| author | Li, Meilin He, Ji Yu, Yi Xu, Jia Lei, Shanzhe Teng, Yan Wang, Yingchun Wang, Xuhong |
| author_facet | Li, Meilin He, Ji Yu, Yi Xu, Jia Lei, Shanzhe Teng, Yan Wang, Yingchun Wang, Xuhong |
| contents | The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \emph{Hidden Watermarking} for copyright protection and \emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniMark: Artificial Intelligence Generated Content Identification Toolkit Li, Meilin He, Ji Yu, Yi Xu, Jia Lei, Shanzhe Teng, Yan Wang, Yingchun Wang, Xuhong Cryptography and Security Artificial Intelligence The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \emph{Hidden Watermarking} for copyright protection and \emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem. |
| title | UniMark: Artificial Intelligence Generated Content Identification Toolkit |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2512.12324 |